Method of determining parameters of a sample by X-ray scattering applying an extended genetic algorithm including a movement operator

ABSTRACT

A method of determining parameters of a sample by X-ray scattering comprising the steps of exposing the sample to X-rays and measuring scattered X-ray intensity, generating a parameterized model of the sample which is used for numerical simulation of scattered X-ray intensity on the basis of a physical scattering theory, comparing the experimental and simulated X-ray scattering data to generate an error value, and modifying the parameters of the model by means of a genetic algorithm involving an amount of individuals each with an equal number N of encoded parameters forming a generation and applying the genetic operators of “selection”, “crossover” and “mutation” used for composing successive generations of evolving individuals, is characterized in that from one generation to the next a “movement” genetic operator is applied which moves at least some of the encoded parameters of at least some of the individuals towards the respective encoded parameters of the individual with the smallest error value. The inventive method improves the genetic algorithm such that it can approximate the true sample parameters faster and with better reliability.

This application claims Paris Convention priority of EP 03 022 421.6filed Oct. 7, 2003 the complete disclosure of which is herebyincorporated by reference.

BACKGROUND OF THE INVENTION

The invention relates to a method of determining parameters of a sampleby X-ray scattering comprising the steps of:

-   -   a) exposing the sample to X-rays and measuring scattered X-ray        intensity;    -   b) generating a parameterized model of the sample which is used        for numerical simulation of scattered X-ray intensity on the        basis of a physical scattering theory;    -   c) comparing the experimental and simulated X-ray scattering        data to generate an error value;    -   d) modifying the parameters of the model by means of a genetic        algorithm involving an amount of individuals each with an equal        number N of encoded parameters forming a generation and applying        the genetic operators of “selection”, “crossover” and “mutation”        used for composing successive generations of evolving        individuals.

Such a method has been disclosed in A. Ulyanenkov, K. Omote, J. Harada;Physica B 283 (2000), 237–241.

X-ray analysis is applied in numerous ways in biology, chemistry,physics and materials science. The interaction of x-rays with a samplecan reveal information about the sample that would be difficult toobtain by other means, if at all. Moreover, x-ray analysis is anon-destructive method, making it particularly attractive.

One way to investigate unknown parameters t₁, . . . t_(N) of complexsamples such as multilayer structures by X-ray analysis are the socalled trial and error methods. These methods start with the measurementof an experimental scattering spectrum of the sample. Then, numeroustest structures (each with a different set of parameters defining arespective test structure) are assumed and simulated scattering spectrafor these test structures are calculated on the basis of a physicalmodel (e.g. kinematic scattering theory). The parameters defining thetest structure whose theoretical spectrum has the best match with theexperimental scattering spectrum are considered to be approximately thetrue parameters of the sample. The best match is typically identified asthe global minimum of an error value, in particular a χ² differencefunction of the experimental spectrum and the simulated spectra. Typicalparameters thus approximated include film or layer thickness, latticestrain, contamination or doping level and the like.

In general, it is impossible to calculate simulated spectra for allpossible test structures due to the finite capacity of computers. On thecontrary, it is necessary to provide a decision algorithm determiningthe test structures to be calculated. There is an arbitrarily chosentest structure (or a set of test structures) to begin with, and the nexttest structures to be calculated are chosen by the decision algorithm onthe basis of the quality of match of the previous test structures.

The most common decision algorithms are gradiental methods. If for acertain parameter t_(i) the difference function χ² decreases whenchanging the parameter t_(i) slightly into one direction, then the nextset of parameters is chosen such that t_(i) is shifted slightly in thisdirection. If the difference function χ² increases again, however, theparameter t_(i) is no more changed into said direction. Typicalgradiental methods include the Simplex algorithm, theLevenberg-Marquardt algorithm and the Newton algorithm. However,gradient methods run the risk of getting trapped in local minima notidentical with the global minimum of the difference function, and nouseful approximation for the true parameters of the sample can be foundin such a case.

One type of decision algorithm overcoming this problem is the so calledgenetic algorithm. This decision algorithm works as follows:

In the beginning, there is an arbitrarily chosen starting set of socalled individuals (or chromosomes). Each individual represents a teststructure. These individuals all carry an equal number of so-calledencoded parameters (or genes). The encoded parameters are eachcorrelated to a physical parameter of the test structure, e.g. animpurity level or a layer thickness. Thus the number of encodedparameters corresponds to the number of physical parameters of themodeled sample to be investigated. All individuals as a whole representa population. A typical population comprises 20 to 50 individuals.

During the genetic algorithm, the population is altered. The startingpopulation is transformed into a new population, and this new one isaltered into another one and so on. The populations at different stepsof alteration are called generations. A generation to be altered iscalled a parent generation, and the generation newly created out of theparent generation is called an offspring generation. Thus, the startingpopulation is the first parent generation in the algorithm. Subsequentgenerations of individuals are intended to contain an individual(s)approaching the global minimum of the error value.

For the creation of an offspring generation, at first the error value ofeach individual in the parent generation is calculated. The elite, i.e.a number of typically one or two individuals with the smallest errorvalue in the parent generation, are copied without any alteration intothe offspring generation. Then the individuals of the parent generationare subjected to genetic operators.

These operators include a selection operator which determinesindividuals of the parent generations which are used to produce theoffspring generation.

Another operator is the mutation operator. It decides about changes insingle genes of an offspring individual, in particular which genes andto what degree these genes are altered. In the state of the art, thesemutations are random changes in the genes.

A third operator determines the crossover of the genes of two matingparent individuals (crossover or mating operator). It decides what geneis taken from which parent. In the state of the art, it is known to cutthe parent chromosomes (=individuals) in half, and the upper part of theoffspring individual is chosen as the upper part of the first parentindividual, and the lower part of the offspring individual is chosen asthe lower part of the second parent individual.

Through application of these genetic operators, an unlimited number ofsubsequent generations can be created. Typically, about 10 to 100generations are calculated. In the final (youngest) offspringgeneration, the individual with the lowest error value corresponds tothe test structure with the best match with the true sample structure.The genetic algorithm is very reliable and gives a good approximation tothe true sample structure, with only a low risk of running into a localminimum of the error value function.

In order to increase the final accuracy of the determined sampleparameters, a gradiental method may subsequently be applied, with theset of parameter determined in the genetic algorithm as a starting setof parameters in the gradiental method. This starting set of parametersshould be close enough to the global minimum to avoid trapping in anon-global minimum.

However, the genetic algorithm requires a lot of calculations,determined by the amount of individuals per generation, the number ofgenerations and the complexity of the genetic operators.

It is the object of the present invention to improve the geneticalgorithm such that it can approximate the true sample parametersfaster, i.e. within less generations and/or with less individuals pergeneration, and with better reliability.

SUMMARY OF THE INVENTION

This object is achieved by a method as described above, characterized inthat a “movement” genetic operator is applied from one generation to thenext to move at least some of the encoded parameters of at least some ofthe individuals towards respective encoded parameters of the individualwith the smallest error value.

The proposed movement genetic operator is somewhat similar to themutation operator, but the change in the gene (=encoded parameter) orgenes is not random, but intentional and directed. The alteration of anencoded parameter affected by the inventive movement operator is suchthat the difference between the encoded parameter of the parentindividual and the encoded parameter of the best matching individual ofthe parent generation is larger than the encoded parameter of theoffspring individual and the encoded parameter of said best matchingindividual of the parent generation. In most cases, the inventivemovement will lead to an offspring individual with a lower error valuethan a randomly mutated offspring individual.

The inventive movement of an encoded parameter may be set to the digitalminimum, i.e. the encoded parameter is moved only one digit towards thevalue of the best matching individual, or it may comprise a certainpercentage of the encoded parameter difference, such as 20%. Preferably,the percentage is less or equal to 50%.

It is possible to affect all or just a fraction of the individuals of ageneration by the movement operator. Moreover, it is possible to alterall or only a part of the genes of an affected individual. Preferably,each affected individual is subjected to the movement operator only withrespect to a few, in particular only one encoded parameter.

Note that the inventive movement operator can be applied to a parentindividual immediately before creating an offspring individual, ordirectly at the creation of the offspring individual.

In a particularly preferred variant of the inventive method, after agiven number k of successive generations the genetic operator of“mutation” is no longer applied in evolution of further generations.

In other words, in the last few generations calculated, the mutationgenetic operator is deactivated. By this means, random changes in thegenes are avoided when the encoded parameters and the respective teststructure are already close to the corresponding desired true structure.During creation of the last generations, random changes most oftenworsen the quality of match of the corresponding test structure and leadaway from the wanted global minimum of the error value function.

When dispensing with the random changes during the last generations inaccordance with the invention, the exact global minimum can beapproximated by the genetic algorithm with a higher accuracy, since onlygenes already close to this minimum are available for new combinationsin the offspring generations. The inventive genetic algorithm during thelast generations can replace a subsequent application of a gradientalmethod and thus save calculation power and time.

When a total number of G generations is created by the inventive geneticalgorithm, and after k generations the mutation operator is no moreapplied, then k may be chosen such that a minimum number of generationsG−k is performed without the mutation operator, for example with G−k≧5,or such that a minimum fraction of generations (G−k)/G is performedwithout the mutation operator, for example (G−k)/G≧20%. Alternatively orin addition, it is also possible that the transition generation (i.e.the determination of k) can be coupled to a minimum quality of match ofthe best fitting individual of the transition generation: as soon as thequality of match of the best fitting individual of a generation reachesa limiting value, subsequent generations are generated without themutation operator.

In a preferred variant, at least some of the offspring individuals ofevery successive generation evolve from more than two parent individualsof the respective previous generation. In particular, genes from threeparent individuals can be combined to generate a new offspringindividual. By this means, the genes of selected individuals can bemixed in less generations, and a wider parameter space can be explored.

Further preferred is a variant of the inventive method, wherein at leastsome of the offspring individuals of every successive generation evolvefrom only one parent individual of the respective previous generation(=“cloning”). Genetic operators such as mutation and movement may stillapply, however. Thus, successful gene combinations can be kept even whenthey do not belong to a kept elite, and can be slightly changed with ahigh chance of being an individual of low error value in the offspringgeneration again.

In an alternative and advantageous variant of the inventive method, the“crossover” genetic operator is modified such that the encodedparameters of each offspring individual is selected randomly from therespective encoded parameters of the parent individuals of the previousgeneration. The vector describing the random selection of the parentindividual for each gene is called the random chromosome. The randomchoice of the genes accelerates the mixing of selected successful genesand promotes the exploration of a wide parameter space.

It is particularly advantageous for a variant if from one generation tothe next at least the individual with the smallest error value istransferred unchanged (=“elite”). By this means the individual with thelowest error value in the offspring generation always has a lower orequally low error value as the individual with the lowest error value inthe parent generation. Therefore the matching of the best matchingindividuals can only improve in the course of the generations.

A preferred variant of the inventive method provides that the errorvalue is generated by means of a weighting function, in particular basedon logarithmic values of the scattering data. By means of thelogarithmic scale, an overestimation of regions of high absolute valuesin the error value function is avoided. The weighting function ispreferably a logarithmically scaled instead of absolute x-ray intensitywithin the χ² difference function.

Further advantages can be extracted from the description and theenclosed drawings. The features mentioned above and below can be used inaccordance with the invention either individually or collectively in anycombination. The embodiments mentioned are not to be understood asexhaustive enumeration but rather have exemplary character fordescription of the invention.

The invention is shown in the drawings:

BRIEF DESCRIPTION OF THE DRAWING

FIG. 1 shows a flowchart of a genetic algorithm (=CGA, Classical GA)according to the state of the art;

FIG. 2 shows a flowchart of an inventive extended genetic algorithm(=XGA);

FIG. 3 shows a graphic representation of an analytical function used totest the inventive method, the model function having numerous localmaxima along with a hardly recognizable global one;

FIG. 4 shows a convergence chart for the CGA, CGA+movement operator, andXGA for the analytical function of FIG. 3;

FIG. 5 shows an X-ray reflectivity diagram of a Au/Fe₃O₄/MgO sample,with experimental results dotted and XGA simulation results as acontinuous line;

FIG. 6 shows the convergence of the χ² function for CGA, XGA, SimulatedAnnealing and Simplex Method when fitting the experimental x-rayreflectivity data of FIG. 5, wherein the iteration scale of SimulatedAnnealing and Simplex Method techniques is reduced to the GA generationsscale on the principle of equal computation time;

FIG. 7 shows an X-ray diffraction diagram of a Si_(1-x)Ge_(x)/Si(004)Bragg reflection diagram, with experimental results dotted and XGAsimulation results as a continuous line;

FIG. 8 shows the convergence of the χ² function for XGA and SimulatedAnnealing when fitting the experimental x-ray diffraction data of FIG.7, wherein the iteration scale of the Simulated Annealing method isreduced to the GA generations scale on the principle of equalcomputation time.

DESCRIPTION OF THE PREFERRED EMBODIMENT

In the following, the inventive extended genetic algorithm and itsapplication to x-ray analysis are explained in detail.

The classic scheme of genetic algorithm is extended to improve therobustness and efficiency of the method. New genetic operatorsimplemented in this work are shown to increase the convergence speed andreliability of the optimization process. A complex model function withmultiple local extrema and real x-ray reflectivity and diffraction datahave been used to testify the modified algorithm. The effectiveness ofthe new technique is compared to other optimization methods.

X-ray metrological methods in science and industry have been proved tobe efficient techniques for sample characterization and growth processdevelopment and control. A large variety of sample structures can beprobed by x-rays to examine film thickness, interface roughness,crystallographic lattice strain and distortion, material contamination,etc. Although the measured x-ray data can be used directly forevaluation of some sample parameters, the detailed knowledge about thesample structure can only be obtained using special data treatmentprocedures. These procedures usually utilize a trial-and-errortechnique, which uses a parameterized sample model for the simulation ofthe x-ray scattering process, and then the difference χ² betweencalculated and measured intensities is minimized in view of the sampleparameters. Thus, an effective and robust optimization algorithm isrequired for accurate data interpretation. Moreover, in most ofexperimental setups, x-ray measurements provide the magnitude ofscattered x-ray intensity, whereas the amplitude and the phase of x-raysare lost, and that is why no procedures are available for reconstructionof sample physical parameters directly from measured intensities. Theloss of x-ray phase information during the measurements causes theambiguity of data interpretation results, e.g. when several samplemodels result in a comparably small difference χ² between measurementsand simulations. This situation requires from the optimization techniqueto be able to find not only a single global minimum, but also a set ofdeepest minima on the χ² hypersurface, which may contain real physicalsolutions. Genetic algorithms (=GA) [Ref. 1], widely used nowadays, seemto be most successful in solving all mentioned problems in x-ray dataanalysis. They combine a stochastic search and strategy aimed featuresthat help to find the global minimum along with other local minima ofthe cost function χ² of comparable magnitude.

In the present work, we propose specific modifications of the classic GAscheme, which improve the efficiency of the algorithm. These newimplementations into standard GA have been testified and the results arecompared with results obtained by classic GA (CGA). As the test objects,a complex model function possessing multiple minima, and real x-raydiffraction and reflection measurements have been used. We describe andqualitatively ground the implemented modifications of the geneticalgorithm. Test results of the proposed method are presented calledfurther extended GA (XGA) for model multi-minima function and thecomparison with CGA. The real x-ray diffraction and reflectionmeasurements are fitted by using extended GA and various methods, andthe performance of XGA is compared with other optimization techniques[Ref. 4].

An explanation of the details of genetic algorithms can be found innumerous publications (see, e.g. [Ref. 1] and citations therein), and inthis section only the principle construction of a classic GA isdescribed. Genetic algorithms exploit the Darwin's evolutionary idea andthe principle of survival of most adapted individuals in a population.The first step in any GA application is the formalization of theparameters to be investigated in order to unify all the operations withthem. Usually, the binary representation of the whole parameter space isused, that allows to easily code the problem. Every point in theparameter space then represents a unique physical state of theinvestigated system, and the goal is to explore this space to find thepoint which gives the best fit according to the pre-defined fitnesscriteria. Since the fitted-parameters are formalized, any set of themcomprehensively describing the system is considered as an individual;the limited number of individuals compose a population, which isevolving on the basis of some genetic rules. The procedure of parameterformalization is called encoding, whereas reverse operation of obtainingthe physical parameters is a decoding.

The typical GA search procedure consists of the following steps,depicted in FIG. 1. Firstly, the random population of individuals, whichare the points in the parameter space or, the same, the single set ofthe system fitted parameters, is created (the size of the population isan internal GA parameter). Then the population begins to evolve by theproduction of new generations, i.e. the creation of the successivepopulations on the basis of the primary one. The most principle rules(called also GA operators) used for the creation of a new generation ofindividuals are selection of parents, crossover and mutation operators.The first operator regulates the parents selection procedure for theproduction of offsprings for the new generation. The second ruledescribes how the parents hand down their features (single parameters,genes) to children. The mutation operator provides irregular changes ofoffsprings to strengthen a statistical nature of GA. Applying thesebasic operators along with other optional rules (for example, elitism,i.e. the transfer of the fittest individuals from the current generationto the successive generation without any changes), after a certainnumber of generations GA delivers the fittest individual, i.e. the setof parameters at which the system satisfies the fitness criteria in thebest way. The probability to find the best existing set of parameters byGA increases with the number of generations evolved, i.e. with thelength of evolution.

All of the operators described above have various implementations andinternal parameters, which are used to optimize the GA efficiency fordifferent applications. The genetic algorithm, being correctly tuned forthe concrete problem, demonstrates excellent results as an optimizationand search procedure [Ref. 2]. In this work, we propose furthermodifications of basic GA operators and general rules to increase theefficiency and reliability of the algorithm. These modifications are:

[1.] One more basic GA operator is implemented, the movement operator.Before the selection of parent individuals for mating, a limited numberof parents are moved towards the best individual in the population, i.e.the parameters of the moved parents are changed closer to the parametersof the fittest individual. This operator improves the convergence of GA,by increasing the amount of individuals in the vicinity of the fittestone.

[2.] Each offspring can have not only two parents but arbitrary numberof parents. In our case, we particularly propose a number of parentsn=1, 2, 3. This multiple choice combines the classic two-parentalapproach with single “cloning” (n=1) of parents, which improves theconvergence of GA by increasing an amount of individuals with betterfitness, and multi-parental options (n=3), which increase the variety ofa population by wider exploration of the parameter space.

[3.] A new crossover principle, providing a better mixture of parents'features in children, is implemented by using a random sequence of bits,the so called random chromosome. When parents mate, the offspringinherits the parameter from that parent, to which the corresponding bitin the random chromosome points to. This modification results in a moreefficient parameter mixture in the crossover procedure, optimizes thesource code and accelerates the algorithm because only simple booleanoperations are involved (in particular in the case of two parents).

[4.] A more effective principle for the formation of new generations isdeveloped. The sequence of formation is the following: firstly, thelimited number of elite individuals is moved to the successivepopulation; then the population is filled by the individuals from theprevious generation and by new offsprings randomly, with a probabilitydepending on the individual's fitness and the number of children left.This improvement prevents individuals with good fitness from beingreplaced by new children and replaces the individuals with rather badfitness, i.e. increasing the convergence and decreasing the probabilityfor the best solutions to be skipped.

[5.] The mutations are forbidden in a given number of last generations.This modification permits to use solely GA for a fine parameter fittingcycle. Because of their stochastic nature, conventional GA schemesrarely deliver exactly the point with the best fitness in the parameterspace. Usually, the gradiental optimization methods have to be furtherapplied in the vicinity of the solution found by GA to refine theparameters [Ref. 2] (see flowchart in FIG. 1). For example, if thefitness criterion is based on some cost function (typically, thedifference between experimental and simulated data), and the aim of GAis to find the global minimum of this cost function, the final solutionis usually found by GA near this minimum point. A gradiental method likeLevenberg-Marquardt has to be used to “slide down” the solution onto thebottom of the function χ². Our fifth modification allows to preciselylocalize the final solution with an accuracy suitable for datainterpretation, which reduces the overall optimization time incomparison with the cascade use of GA and gradiental techniques.

Thus, our modified XGA version of genetic algorithm has the followingprinciple scheme (flowchart in FIG. 2):

-   (i) generation of a random population consisting of S individuals    (population size);-   (ii) evaluation of fitness;-   (iii) movement of M individuals towards the fittest one;-   (iv) creation of C children by using one parent (p₁, cloning), two    parents (p₂, crossover through random gene), and three parents (p₃),    C=p₁+p₂+p₃;-   (v) if mutations are permitted in current generation, m mutations of    parameters are carried out, reach being an inversion of a random bit    in an corresponding children's parameter;-   (vi) a new population is created.

To optimize a source code, the new generation is constructed on thebasis of the previous one, firstly by selection of unchangeable elite Eindividuals, then by substitution of old individuals by C children independence on the fitness of replaced individuals and the number ofremained children. The evolution proceeds by repeating the cycle in FIG.2 either G times (number of generations) or until a requested fitnesstolerance is reached. In the last F generations, the mutations areforbidden to refine the fit results.

To study an effectiveness of above-mentioned XGA implementations, weused firstly a smooth two-dimensional analytical functionz(x,y)=−[(x−0.75)²+(y−0.625)²]*[2−cos(100(x−0.75))−cos(100(y−0.625))]possessing single global maximum at (x;y)=(0.75; 0.625) and multiplelocal maxima (FIG. 3); the function is defined within the interval[x;y]=[0 . . . 1; 0 . . . 1].

FIG. 4 shows the convergence diagram for CGA, CGA with only movements,and full-featured XGA. Evidently, XGA finds global maxima faster thanother modifications at equal conditions (the population consists of 200individuals). We also carried out the tests with a more complicatedmodel function, viz. linear combination of several functions similar tothe depicted one in FIG. 3, but with different values of the constants.Then the statistical error of trapping in the wrong maxima has beenevaluated for CGA and XGA by performing multiple runs of bothalgorithms. The error for CGA is found to be 28% against 4% for XGA, andthus XGA demonstrates higher reliability in comparison with a classic GAscheme.

XGA has been also applied for the fitting of real experimental data fromx-ray reflectivity (XRR) and high-resolution x-ray diffraction (HRXRD),the two most commonly used x-ray techniques. The convergence and speedof XGA are compared with other known methods, simulated annealing (SA)and the simplex method (SM). FIG. 5 shows measured (open dots) andsimulated (solid lines) x-ray reflectivity from gold and magnetite thinsolid films on MgO substrate at a wavelength λ=0.154056 nm. The samplemodel consisting of the sequence Au/Fe₃O₄/MgO with the nominalthicknesses 55 nm/120 nm/substrate and some roughness at the interfacesbetween the layers has been used for fitting of reflectivity datasimulated by Parratt's formalism [Ref. 3] to the measured curves. Thefitted parameters were the thicknesses of the layers t_(Au) andt_(Fe3O4) and the roughnesses of the sample surface and the interfaces.All the methods (CGA, SA, SM, XGA) resulted in an acceptable fitness ofcurves with slight differences in the refined parameters which, however,are within the precision of experimental data. The values of thethicknesses and roughnesses are found to be t_(Au)≅53.8 nm,t_(Fe3O4)≅146.3 nm, σ_(surf)≅0.78 nm, σ_(Au/Fe3O4)≅0.1 nm,σ_(Fe3O4/MgO)≅0.3 nm. However, the effectiveness of the methods isevidently different, as follows from the FIG. 6 showing the χ²convergence diagrams for each used technique. To adjust the time scalesof algorithms, the iteration scales of SA and SM are brought intocorrespondence to GA's generations scale by multiplying them by theratio of computation times t_(GA/SA) and t_(GA/SM). Diagrams show thatXGA finds the best available solution faster than the other methods.FIG. 6 demonstrates also the advantage of XGA due to the fifthimplementation from the Section II, i.e. the prohibition of mutation inthe final stage of evolution. After 100 generations, CGA is stilllocalized in the vicinity the global minimum of χ², whereas XGA hasalready found the point, recognized by simplex method as an exact globalminimum (with certain tolerance), due to ban of mutations after the 60thgeneration. Thus, gradiental methods must not be used to refine a fit.

X-ray diffraction measurements (open dots) from a Si_(1-x)Ge_(x)/Si(004) Bragg reflection at wavelength λ=0.154056 nm are shown in FIG. 7.Two parameters, the thickness of the Si_(1-x)Ge_(x)/Si layer and theconcentration of germanium x, have been fitted (solid line in FIG. 7).The discrepancy between the curve simulated by dynamical diffractiontheory and the experimental data on the right wing of the substrate peakare caused by a diffuse x-ray scattering, which is not taken intoaccount in the present calculations. Both SA and XGA methods used forminimization of the χ² function lead to close values of thicknesst_(SiGe)≅65.7 nm and concentration x≅9.2%. However, the χ² convergencecharts in the FIG. 8 clearly demonstrate the advantage of XGA in speed(SA iterations are resealed by time ratio factor).

The inventive extended genetic algorithm is shown to be the mosteffective and robust optimization technique in comparison with theclassic genetic algorithm, simulated annealing and the simplex method.Phenomenological tests with complex model functions possessing multipleextrema, as well as with real experimental x-ray data, both reflectivityand diffraction, have shown the advantages of XGA. In view of largecomputer resources required for fitting of x-ray data, the robustnessand quickness of XGA plays an essential role for precise data analysisin x-ray analytical software.

REFERENCES

-   [Ref. 1] D. E. Goldberg, Genetic Algorithms in Search, Optimization    and Machine Learning, Addison-Wesley, Reading, Mass., 1989.-   [Ref. 2] A. Ulyanenkov, K. Omote, J. Harada, Physica B 283 (2000)    237.-   [Ref. 3] L. G. Parratt, Phys. Rev. 95, 359 (1954)-   [Ref. 4] F. H. Walters, L. R. Parker, S. L. Morgan and S. N. Deming,    Sequential Simplex Optimization, CRC Press, Boca Raton, Fla.,    1991; H. L. Aarts and J. Kost, Simulated Annealing and Bolzman    Machines, John Wiley, New York, 1989

1. A method for determining parameters of a sample by X-ray scattering,the method comprising the steps of: a) exposing the sample to X-rays andmeasuring scattered X-ray intensity; b) generating a parameterized modelof the sample which is used for numerical simulation of scattered X-rayintensity on a basis of a physical scattering theory; c) comparingexperimental and simulated X-ray scattering data to generate an errorvalue; d) modifying parameters of the model by means of a geneticalgorithm involving a number of individuals each having an equal numberN of encoded parameters and forming a generation, and applying geneticoperators of “selection”, “crossover” and “mutation” to composesuccessive generations of evolving individuals, wherein a “movement”genetic operator is applied from one generation to the next which movesat least some of the encoded parameters of at least some of theindividuals towards respective encoded parameters of an individualhaving a smallest error value.
 2. The method of claim 1, wherein thegenetic operator of “mutation” is no longer applied in evolution offurther generations after a given number k of successive generations. 3.The method of claim 2, wherein at least some offspring individuals ofeach successive generation evolve from more than 2 parent individuals ofa respective previous generation.
 4. The method of claim 1, wherein atleast some offspring individuals of each successive generation evolvefmm only 1 parent individual of a respective previous generation(=“cloning”).
 5. The method of claim 1, wherein the “crossovers” geneticoperator is modified such that encoded parameters of each offspringindividual are selected randomly from respective encoded parameters ofparent individuals of a previous generation.
 6. The method of claim 1,wherein at least one individual with a smallest error value istransferred unchanged (=“elite”) from one generation to a next.
 7. Themethod of claim 1, wherein an error value is generated by means of aweighting function.
 8. The method of claim 7, wherein said weightingfunction is based on logarithmic values of the scattering data.